Generator excitation system remodeling transformation method, system and equipment and storage medium

Abstract

The invention, which relates to the technical field of generator excitation control and digital twinborn modeling, discloses a generator excitation system remodeling and transformation method, system and device, and a storage medium, comprising the following steps: taking unit operation data to perform synchronous filtering, and generating excitation system digital twinborn model parameters according to a synchronous filtering result; a control response sequence is obtained through the model parameters and corrected, and excitation system operation verification is conducted according to the corrected control response sequence. According to the method, unified time sequence processing and dynamic parameter modeling of excitation voltage, current and rotating speed signals are realized through synchronous filtering and digital twin modeling, so that the data consistency and parameter analysis precision of an excitation system are improved; and better effects are achieved in the aspects of modeling precision, parameter updating reliability and operation verification consistency.

Description

A method, system, equipment, and storage medium for upgrading and retrofitting a generator excitation system. Technical Field

[0001] This invention relates to the technical field of generator excitation control and digital twin modeling, specifically to a method, system, equipment, and storage medium for the transformation and upgrading of a generator excitation system. Background Technology

[0002] During the upgrade and renovation of generating units, the parameter configuration, control logic, and feedback loop of the excitation system all need to be re-matched. However, most existing methods for upgrading excitation systems rely on empirical parameter adjustments or local simulation calculations, lacking a unified data synchronization mechanism and overall dynamic modeling methods, making it difficult to achieve accurate parameter mapping and control matching in the early stages of the upgrade. Furthermore, since traditional models are mostly based on static assumptions, they cannot reflect changes in the unit's operating status in real time, resulting in time lags and accumulated deviations in parameter updates and control responses.

[0003] With the development of digital twin technology and intelligent control theory, real-time synchronization and linkage between the physical system and the virtual model can be achieved by high-frequency acquisition and dynamic fitting modeling of unit operation data.

[0004] In the scenario of excitation system transformation, there is still a lack of systematic solutions on how to achieve time alignment of multi-source operating data through synchronous filtering, thereby constructing a digital twin model with dynamic and updatable characteristics, and completing excitation parameter updates and rectification balancing calculations under the transformation control logic. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing methods for upgrading and transforming generator excitation systems suffer from issues such as parameter matching relying on manual experience, poor data synchronization, high model update delays, and the need to achieve synchronous filtering of multi-source operating data during the upgrade process, establish digital twin model parameters of the excitation system, and realize dynamic updating of excitation parameters and rectification balance calculation under the new control logic.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for upgrading and retrofitting a generator excitation system, comprising taking the unit's operating data for synchronous filtering, and generating digital twin model parameters of the excitation system based on the synchronous filtering results.

[0008] The control response sequence is obtained by model parameters and corrected. The excitation system operation is then verified based on the corrected control response sequence.

[0009] Among them, by fitting and modeling the time-series characteristics of synchronous filtering data, the dynamic correlation between excitation voltage, current and speed is determined.

[0010] The excitation parameter update and rectification control equalization operations are performed on the model parameters to obtain the control response sequence.

[0011] During the correction process, the parameter error of the control response sequence is minimized, and the control response sequence after the parameter error minimization calculation is used as the input for the operation verification of the excitation system.

[0012] As a preferred embodiment of the generator excitation system transformation and upgrading method described in this invention, the synchronous filtering includes converting the excitation voltage signal, excitation current signal and speed signal taken from the generator unit into time-series data with a unified sampling frequency.

[0013] Time alignment operations are performed within the same time window to synchronously sample different types of signals.

[0014] A sliding weighted filtering algorithm is applied to each set of synchronization signals to calculate the weighted sum of continuous samples and smooth transient fluctuations.

[0015] The smoothed sequence generated after filtering is stored in the form of a time index.

[0016] As a preferred embodiment of the generator excitation system transformation and upgrading method of the present invention, the fitting modeling includes receiving time series data output by synchronous filtering and constructing the time series function relationship of the excitation system.

[0017] Establish a set of multivariable functions.

[0018] Define the time delay term and weighting factor to describe the response hysteresis behavior of the excitation characteristics.

[0019] The fitting coefficients are calculated using the recursive least squares method, and the fitting error is output at the end of each iteration.

[0020] Generate a parameter matrix.

[0021] As a preferred embodiment of the generator excitation system transformation method of the present invention, the generation of digital twin model parameters of the excitation system includes parameter normalization and structure transformation of the parameter matrix.

[0022] Perform standardized calculations on each parameter.

[0023] Perform interactive mapping on each parameter group and define the parameter mapping function.

[0024] Discrete time-series signals are mapped to a set of continuous model parameters using a parameter mapping function.

[0025] Transform the set of parameters of a continuous model into the set of parameters of a digital twin model.

[0026] As a preferred embodiment of the generator excitation system transformation and upgrading method of the present invention, the method of obtaining the control response sequence includes taking the generated digital twin model parameter set as input and performing excitation parameter update and rectification control equalization calculation.

[0027] The excitation parameter update includes establishing a parameter mapping matrix, expanding the model parameters according to the time dimension, and corresponding to the parameter positions of each feedback channel in the control logic.

[0028] The parameter update step size is set according to the control structure after the change, and the excitation parameters are updated in each iteration cycle.

[0029] The rectifier control equalization operation includes calculating the relative deviation rate of the three-phase excitation current.

[0030] The deviation direction is obtained based on the relative deviation rate of the three-phase excitation current, and the current distribution factor is adjusted accordingly.

[0031] The updated excitation parameters and current distribution factor together generate a set of control variables, which are recorded over time and form a control response sequence.

[0032] As a preferred embodiment of the generator excitation system transformation and upgrading method of the present invention, the step of minimizing the parameter error of the control response sequence includes introducing a reference response sequence for comparison into the control response sequence input error calculation logic.

[0033] Define an error function, calculate the cumulative error by integrating the error function over the time interval, and use the cumulative error as the target quantity for iterative updates.

[0034] Set the learning rate and convergence factor, and adjust the parameter update amount according to the direction of the error gradient.

[0035] If the rate of change of error is lower than the minimum convergence threshold after two consecutive iterations, then the correction completion signal is output.

[0036] When the error increases or oscillations occur, adjust the learning rate and recalculate the cumulative error.

[0037] As a preferred embodiment of the generator excitation system replacement and retrofit method of the present invention, the excitation system operation verification includes inputting the control response sequence after minimizing parameter error calculation into the verification logic to verify the operation consistency of the excitation system hour by hour.

[0038] The verification process includes input recognition, state comparison, logical judgment, and record update.

[0039] Another objective of this invention is to provide a generator excitation system upgrade system that solves the problems of asynchronous data timing, delayed parameter updates, and insufficient rectifier control equalization accuracy in current excitation system upgrade technologies through the collaborative calculation of step filtering data processing and control response sequence correction.

[0040] As a preferred embodiment of the generator excitation system transformation and upgrading system described in this invention, it includes an excitation system modeling module and an excitation control verification module.

[0041] The excitation system modeling module is used to acquire unit operating data for synchronous filtering and generate excitation system digital twin model parameters based on the synchronous filtering results.

[0042] The excitation control verification module is used to obtain and correct the control response sequence through model parameters, and to verify the operation of the excitation system based on the corrected control response sequence.

[0043] Another object of the present invention is to provide a generator excitation system transformation and upgrading device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the generator excitation system transformation and upgrading method.

[0044] Another object of the present invention is to provide a generator excitation system replacement and modification storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the generator excitation system replacement and modification method are implemented.

[0045] The beneficial effects of this invention are as follows: The generator excitation system transformation method provided by this invention achieves unified timing processing and dynamic parameter modeling of excitation voltage, current, and speed signals through synchronous filtering and digital twin modeling, thereby improving the consistency of excitation system data and the accuracy of parameter analysis; through excitation parameter updates and rectifier control balancing, adaptive adjustment of excitation current and periodic calibration of parameters are achieved under the transformation structure, ensuring the coordination and response stability of each feedback channel in the control logic; through control response sequence correction and parameter error minimization calculation, a self-iterative parameter optimization logic is constructed, making the digital twin model dynamically consistent with the actual operating state. This invention achieves better results in terms of modeling accuracy, parameter update reliability, and operational verification consistency. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 is an overall flowchart of a generator excitation system transformation and upgrading method provided in Embodiment 1 of the present invention. Detailed Implementation

[0048] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0049] Example 1, referring to Figure 1, is an embodiment of the present invention, providing a method for upgrading and retrofitting a generator excitation system, including: S1: taking the unit operating data and performing synchronous filtering, and generating digital twin model parameters of the excitation system based on the synchronous filtering results.

[0050] The excitation voltage signal, excitation current signal, and speed signal taken from the generator unit are converted into time-series data with a unified sampling frequency.

[0051] Time alignment operations are performed within the same time window to synchronously sample different types of signals.

[0052] Furthermore, the system performs point-by-point detection of anomalies, drift points, and abrupt changes in the original signal. When the amplitude change rate exceeds the preset amplitude change rate, the system enters the signal correction subprocess.

[0053] The correction sub-process includes calculating a weighted average value according to the trend direction of the previous sampling period and replacing outliers.

[0054] After anomaly removal is completed, a sliding weighted filtering algorithm is applied to each group of synchronization signals to calculate the weighted sum of continuous samples and smooth transient fluctuations.

[0055] A preferred approach for applying the sliding weighted filtering algorithm to each set of synchronization signals is as follows: ,in, This represents the filtered signal. Indicates the filter weights. This represents the half width of the sliding window. Indicates the current sampling time point. This represents the sampling index variable within the weighted summation window. This represents the index of the current sampling time in the time series. This represents a time index variable.

[0056] A preferred scheme for signal correction is: ,in, This indicates the corrected signal output. Indicates the original signal in time The sampled values, Indicates the rate of change of the signal. Indicates the threshold of the rate of change of amplitude. Indicates the trend correction factor. Represents a symbolic function. This indicates otherwise.

[0057] The smoothed sequence generated after filtering is stored in the form of a time index.

[0058] If there are missing samples within a time period in the filtered output, the missing interval is filled using the difference allocation method between adjacent samples.

[0059] Furthermore, the time-series data output by the synchronous filter is received to construct the timing function relationship of the excitation system.

[0060] A multivariable function set is established with voltage signal, excitation current signal and speed signal as independent variables and the change in electromotive force of system response as dependent variable.

[0061] A preferred approach to establishing a multivariable function set is as follows: ,in, This represents the electromotive force output of the excitation system. This represents the corrected excitation voltage signal. This represents the corrected excitation current signal. This indicates the corrected rotational speed signal. , , These represent the response weighting coefficients for three different signals. , , These represent three different delay terms. Represents the fitting error term. Indicates a time index.

[0062] By analyzing the time dependencies between variables, a time delay term and a weighting factor are defined to describe the response hysteresis behavior of the excitation characteristics.

[0063] For each time period, parameter identification is performed, the fitting coefficients are calculated using the recursive least squares method, and the fitting error is output at the end of each iteration.

[0064] When the rate of change of the error in two consecutive iterations is lower than the minimum iteration error, the model is considered to have converged and the fitted parameter set is output.

[0065] When the error fails to converge or the fitting residuals increase abnormally, the model weight adjustment logic is executed to reallocate the weights of each variable and improve the matching accuracy.

[0066] Generate a parameter matrix.

[0067] Furthermore, parameter normalization and structural transformation are performed on the parameter matrix.

[0068] A preferred approach to parameter normalization and structure transformation is as follows: ,in, Indicates the first One normalized parameter, This represents the parameter normalization function. Represents the original parameter variable. This represents the mean of the parameter. Indicates the standard deviation of the parameter. Represents the voltage weighting matrix. Represents the current weighting matrix. Represents the rotational speed weight matrix. Indicates the first in the voltage parameter group One element, Indicates the first current parameter group One element, Indicates the first parameter in the speed parameter group One element, This represents the parameter vector of the digital twin model.

[0069] Standardization is performed on each parameter to ensure that physical quantities with different dimensions are calculated on a uniform scale.

[0070] The normalized parameters are divided into voltage-related groups, current-related groups, and speed-related groups.

[0071] The voltage-related group, current-related group, and speed-related group correspond to the electromagnetic characteristics, excitation response, and mechanical coupling of the excitation system.

[0072] Perform interactive mapping on each parameter group and define the parameter mapping function.

[0073] Discrete time-series signals are mapped to a set of continuous model parameters using a parameter mapping function.

[0074] Transform the continuous model parameter set into a digital twin model parameter set with dynamic and updatable characteristics.

[0075] S2: Obtain the control response sequence through the model parameters and correct it. Then, verify the operation of the excitation system based on the corrected control response sequence.

[0076] The generated set of digital twin model parameters is used as input to perform excitation parameter updates and rectifier control equalization calculations.

[0077] A preferred scheme for performing excitation parameter updates and rectifier control balancing calculations is as follows: ,in, Represents the control response vector. Represents the model parameter matrix. Represents the real-time control variable vector. Represents the learning coefficient. This represents the gradient with respect to the electromotive force error. This represents the control variable from the previous period. Indicates the balance adjustment step size. This represents the three-phase excitation current vector. Indicates the average excitation current. This indicates the timing of the control update.

[0078] The excitation parameter update includes establishing a parameter mapping matrix, expanding the model parameters according to the time dimension, and corresponding to the parameter positions of each feedback channel in the control logic.

[0079] The parameter update step size is set according to the control structure after the change, and the excitation parameters are updated in each iteration cycle.

[0080] The rectifier control equalization operation includes calculating the relative deviation rate of the three-phase excitation current.

[0081] The deviation direction is obtained based on the relative deviation rate of the three-phase excitation current, and the current distribution factor is adjusted so that the current sequence converges to the equilibrium state in subsequent iterations.

[0082] The updated excitation parameters and current distribution factor together generate a set of control variables, which are recorded over time and form a control response sequence.

[0083] Furthermore, a reference response sequence is introduced for comparison in the logic for calculating the input error of the control response sequence.

[0084] Define an error function, calculate the cumulative error by integrating the error function over the time interval, and use the cumulative error as the target quantity for iterative updates.

[0085] Set the learning rate and convergence factor, and adjust the parameter update amount according to the direction of the error gradient.

[0086] If the rate of change of error is lower than the minimum convergence threshold after two consecutive iterations, then the correction completion signal is output.

[0087] When the error increases or oscillations occur, adjust the learning rate and recalculate the cumulative error.

[0088] A preferred method for calculating cumulative error is: ,in, This represents the cumulative error function. Indicates the current control response sequence. Indicates the reference control response sequence. This represents the square operation of the L2 norm. This indicates the current error value. This represents the error value from the previous iteration. Indicates the rate of change of error. This indicates the end time of the filtering calculation. This indicates the start time of the filtering calculation.

[0089] Furthermore, the control response sequence calculated with the parameter error minimized is input into the verification logic to verify the operational consistency of the excitation system hour by hour.

[0090] The verification process includes input recognition, state comparison, logical judgment, and record update.

[0091] The input recognition phase includes checking the time index and data integrity of the response sequence.

[0092] The state comparison phase includes matching the corrected parameters with the model baseline parameters point by point.

[0093] The logical determination stage includes calculating consistency indicators based on parameter matching rate and response stability.

[0094] The record update phase includes writing the verification results into the verification sequence in matrix form.

[0095] Example 2 is an embodiment of the present invention, which provides a generator excitation system transformation and upgrading system, including an excitation system modeling module and an excitation control verification module.

[0096] The excitation system modeling module is used to acquire unit operating data for synchronous filtering and generate digital twin model parameters of the excitation system based on the synchronous filtering results.

[0097] The excitation control verification module is used to obtain and correct the control response sequence through model parameters, and to verify the operation of the excitation system based on the corrected control response sequence.

[0098] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the generator excitation system transformation and upgrading method proposed in the above embodiment.

[0099] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the generator excitation system transformation and upgrading method proposed in the above embodiments.

[0100] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0102] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0103] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for upgrading and retrofitting a generator excitation system, characterized in that, include: Take the unit's operating data and perform synchronous filtering. Generate the parameters of the digital twin model of the excitation system based on the synchronous filtering results. The control response sequence is obtained by modeling parameters and then corrected. The excitation system operation is verified based on the corrected control response sequence. Specifically, the dynamic correlation between excitation voltage, current and speed is determined by fitting and modeling the time-series characteristics of synchronous filter data. The control response sequence is obtained by performing excitation parameter update and rectifier control equalization operations on the model parameters. During the correction process, the parameter error of the control response sequence is minimized, and the control response sequence after the parameter error minimization calculation is used as the input for the operation verification of the excitation system.

2. The method for upgrading and modifying the generator excitation system as described in claim 1, characterized in that: The synchronization filtering includes converting the excitation voltage signal, excitation current signal, and speed signal taken from the generator into time-series data with a unified sampling frequency; performing time alignment operation within the same time window to synchronously sample different types of signals; applying a sliding weighted filtering algorithm to each group of synchronization signals to calculate the weighted sum of continuous samples and smooth transient fluctuations; and storing the smoothed sequence generated after filtering in the form of a time index.

3. The method for upgrading and modifying the generator excitation system as described in claim 1 or 2, characterized in that: The fitting modeling includes receiving time series data output by synchronous filtering, constructing the time series function relationship of the excitation system; establishing a multivariate function set; defining time delay terms and weighting factors to describe the response hysteresis behavior of the excitation characteristics; calculating the fitting coefficients using the recursive least squares method, and outputting the fitting error at the end of each iteration. Generate a parameter matrix.

4. The generator excitation system replacement and retrofit method as described in claim 3, characterized in that: The parameters for generating the digital twin model of the excitation system include parameter normalization and structure transformation of the parameter matrix; Perform normalization operations on each parameter; Perform interactive mapping on each parameter group and define the parameter mapping function; map the discrete time series signal into a continuous model parameter set through the parameter mapping function; and transform the continuous model parameter set into a digital twin model parameter set.

5. The method for upgrading and modifying the generator excitation system as described in claim 1, 2, or 4, characterized in that: The obtained control response sequence includes taking the generated digital twin model parameter set as input and performing excitation parameter update and rectifier control equalization operations; the excitation parameter update includes establishing a parameter mapping matrix, expanding the model parameters according to the time dimension, corresponding to the parameter positions of each feedback channel in the control logic; Based on the modified control structure, the parameter update step size is set, and the excitation parameters are updated in each iteration cycle. The rectifier control equalization operation includes calculating the relative deviation rate of the three-phase excitation current; obtaining the deviation direction based on the relative deviation rate of the three-phase excitation current and adjusting the current distribution factor; the updated excitation parameters and the current distribution factor together generate a control variable set, and the control variable set is recorded over time to form a control response sequence.

6. The generator excitation system replacement and upgrading method as described in claim 5, characterized in that: The calculation of minimizing parameter error of the control response sequence includes inputting the control response sequence into the error calculation logic and introducing a reference response sequence for comparison. definition The error function is calculated by integrating the error function over the time interval, and the cumulative error is used as the target quantity for iterative updates. Set the learning rate and convergence factor, and adjust the parameter update amount according to the error gradient direction; if the error change rate is lower than the minimum convergence threshold after two consecutive iterations, output the correction completion signal; when the error rises or oscillates, adjust the learning rate and recalculate the cumulative error.

7. The method for upgrading and modifying the generator excitation system as described in claims 1, 2, 4 or 6, characterized in that: The excitation system operation verification includes inputting the control response sequence calculated to minimize parameter errors into the verification logic to verify the operational consistency of the excitation system hour by hour; the verification process includes input identification, state comparison, logic judgment, and record update.

8. A generator excitation system replacement and retrofit system, employing the generator excitation system replacement and retrofit method as described in any one of claims 1 to 7, characterized in that: It includes an excitation system modeling module and an excitation control verification module. The excitation system modeling module is used to acquire unit operating data for synchronous filtering and generate digital twin model parameters of the excitation system based on the synchronous filtering results. The excitation control verification module is used to obtain the control response sequence through the model parameters and correct it, and to verify the operation of the excitation system based on the corrected control response sequence.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the generator excitation system transformation and upgrading method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the generator excitation system transformation and upgrading method according to any one of claims 1 to 7.

Images